ArticleArchives of disease in childhood. Fetal and neonatal edition2025
Validation of a machine learning algorithm for identifying infants at risk of hypoxic ischaemic encephalopathy in a large unseen data set.
Article in Archives of disease in childhood. Fetal and neonatal edition, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Developing and Validating a Machine Learning Model to Predict Brain Injury in Preterm Infants Using Multisource Data from the Early Postnatal Period.Children (Basel, Switzerland) · 2026Article
- Blood lactate kinetics as biomarkers of MRI brain injury in neonatal encephalopathy.Pediatric research · 2026Article
- From differential to definitive: diagnosing and treatment of neonatal encephalopathy.Pediatric research · 2025Article
- Review of Precision Medicine and Diagnosis of Neonatal Illness.Diagnostics (Basel, Switzerland) · 2025Review
- Effective classification for neonatal brain injury using EEG feature selection based on elastic net regression and improved crow search algorithm.PeerJ. Computer science · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo validate a hypoxic ischaemic encephalopathy (HIE) prediction algorithm to identify infants at risk of HIE immediately after birth using readily available clinical data.
designSecondary review of electronic health record data of term deliveries from January 2017 to December 2021.
settingA tertiary maternity hospital. PATIENTS: Infants >36 weeks' gestation with the following clinical variables available: Apgar Score at 1 min and 5 min, postnatal pH, base deficit, and lactate values taken within 1 hour of birth
interventionsPreviously trained open-source logistic regression and random forest (RF) prediction algorithms were used to calculate a probability index (PI) for each infant for the occurrence of HIE. MAIN OUTCOME: Validation of a machine learning algorithm to identify infants at risk of HIE in the immediate postnatal period.
results1081 had a complete data set available within 1 hour of birth: 76 (6.95%) with HIE and 1005 non-HIE. Of the 76 infants with HIE, 37 were classified as mild, 29 moderate and 10 severe. The best overall accuracy was seen with the RF model. Median (IQR) PI in the HIE group was 0.70 (0.53-0.86) vs 0.05 (0.02-0.15), (p<0.001) in the non-HIE group. The area under the receiver operating characteristics curve for prediction of HIE=0.926 (0.893-0.959, p<0.001). Using a PI cut-off to optimise sensitivity of 0.30, 936 of the 1081 (86.5%) infants were correctly classified.
conclusionIn a large unseen data set an open-source algorithm could identify infants at risk of HIE in the immediate postnatal period. This may aid focused clinical examination, transfer to tertiary care (if necessary) and timely intervention.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.